Building human motion map with human states estimation in indoor dynamic environments

Yuji Ogawa; Zhidong Wang; Tetsuya Wada; Ken Tomiyama; Yasuhisa Hirata; Kazuhiro Kosuge

doi:10.1109/ROBIO.2011.6181433

Building human motion map with human states estimation in indoor dynamic environments

Yuji Ogawa, Zhidong Wang, Tetsuya Wada, Ken Tomiyama, Yasuhisa Hirata, Kazuhiro Kosuge

ENG - Robotics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

In recent years, robots working in human living space with human-robot interaction are actively researched. To these robots, it is important to perform environmental cognition including to estimate human states around the robots and to have environment map for autonomous motion of the robots. In this research, we focus on building maps of dynamic environment. In this paper, we proposed Human Motion Map, a high-dimensional map, which incorporating human states information into results of conventional SLAM. The Human Motion Map is with high potential on further human estimation and motion planning for robots by considering the uncertainties of environment and human states.

Original language	English
Title of host publication	2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011
Pages	1090-1095
Number of pages	6
DOIs	https://doi.org/10.1109/ROBIO.2011.6181433
Publication status	Published - 2011 Dec 1
Event	2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011 - Phuket, Thailand Duration: 2011 Dec 7 → 2011 Dec 11

Publication series

Name	2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011

Other

Other	2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011
Country/Territory	Thailand
City	Phuket
Period	11/12/7 → 11/12/11

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

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10.1109/ROBIO.2011.6181433

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Ogawa, Y., Wang, Z., Wada, T., Tomiyama, K., Hirata, Y., & Kosuge, K. (2011). Building human motion map with human states estimation in indoor dynamic environments. In 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011 (pp. 1090-1095). [6181433] (2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011). https://doi.org/10.1109/ROBIO.2011.6181433

Building human motion map with human states estimation in indoor dynamic environments. / Ogawa, Yuji; Wang, Zhidong; Wada, Tetsuya et al.

2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011. 2011. p. 1090-1095 6181433 (2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ogawa, Y, Wang, Z, Wada, T, Tomiyama, K, Hirata, Y & Kosuge, K 2011, Building human motion map with human states estimation in indoor dynamic environments. in 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011., 6181433, 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011, pp. 1090-1095, 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011, Phuket, Thailand, 11/12/7. https://doi.org/10.1109/ROBIO.2011.6181433

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abstract = "In recent years, robots working in human living space with human-robot interaction are actively researched. To these robots, it is important to perform environmental cognition including to estimate human states around the robots and to have environment map for autonomous motion of the robots. In this research, we focus on building maps of dynamic environment. In this paper, we proposed Human Motion Map, a high-dimensional map, which incorporating human states information into results of conventional SLAM. The Human Motion Map is with high potential on further human estimation and motion planning for robots by considering the uncertainties of environment and human states.",

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AB - In recent years, robots working in human living space with human-robot interaction are actively researched. To these robots, it is important to perform environmental cognition including to estimate human states around the robots and to have environment map for autonomous motion of the robots. In this research, we focus on building maps of dynamic environment. In this paper, we proposed Human Motion Map, a high-dimensional map, which incorporating human states information into results of conventional SLAM. The Human Motion Map is with high potential on further human estimation and motion planning for robots by considering the uncertainties of environment and human states.

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Building human motion map with human states estimation in indoor dynamic environments

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